From time-series to 2D images for building occupancy prediction using deep transfer learning
<p dir="ltr">Building occupancy information could aid energy preservation while simultaneously maintaining the end-user comfort level. Energy conservation becomes essential since energy resources are scarce and human dependency on appliances is only exponentially increasing. While in...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , |
| منشور في: |
2023
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| الموضوعات: | |
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إضافة وسم
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| _version_ | 1864513543611613184 |
|---|---|
| author | Aya Nabil Sayed (17317006) |
| author2 | Yassine Himeur (14158821) Faycal Bensaali (12427401) |
| author2_role | author author |
| author_facet | Aya Nabil Sayed (17317006) Yassine Himeur (14158821) Faycal Bensaali (12427401) |
| author_role | author |
| dc.creator.none.fl_str_mv | Aya Nabil Sayed (17317006) Yassine Himeur (14158821) Faycal Bensaali (12427401) |
| dc.date.none.fl_str_mv | 2023-03-01T18:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.engappai.2022.105786 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/From_time-series_to_2D_images_for_building_occupancy_prediction_using_deep_transfer_learning/24474652 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electronics, sensors and digital hardware Environmental engineering Information and computing sciences Artificial intelligence Data management and data science Machine learning Occupancy detection Environmental data Feature engineering Image transformation Deep learning Convolutional neural network |
| dc.title.none.fl_str_mv | From time-series to 2D images for building occupancy prediction using deep transfer learning |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Building occupancy information could aid energy preservation while simultaneously maintaining the end-user comfort level. Energy conservation becomes essential since energy resources are scarce and human dependency on appliances is only exponentially increasing. While intrusive sensors (i.e., cameras and microphones) can raise privacy concerns, this paper presents an innovative non-intrusive occupancy detection approach using environmental sensor data (e.g., temperature, humidity, carbon dioxide (CO<sub>2</sub>), and light sensors). The proposed scheme transforms multivariate time-series data into images for better encoding and extracting relevant features. The utilized image transformation method is based on data normalization and matrix conversion. Precisely, by representing time-series in 2D space, an encoding kernel can move in two directions while it can move only in one direction when applied to a 1D signal. Moreover, machine learning (ML) and deep learning (DL) techniques are utilized to classify occupancy patterns. Several simulations are used to evaluate the approach; mainly, we investigated pre-trained and custom convolutional neural network (CNN) models. The latter attained an accuracy of 99.00%. Additionally, pixel data are extracted from the generated images and subjected to traditional ML methods. Throughout the numerous comparison settings, it was observed that the latter strategy provided the optimal balance of 99.42% accuracy performance and minimal training time across the occupancy datasets.</p><h2>Other Information</h2><p dir="ltr">Published in: Engineering Applications of Artificial Intelligence<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.engappai.2022.105786" target="_blank">https://dx.doi.org/10.1016/j.engappai.2022.105786</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_0639dd4b4eeb90ab691041363b17c1ca |
| identifier_str_mv | 10.1016/j.engappai.2022.105786 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24474652 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | From time-series to 2D images for building occupancy prediction using deep transfer learningAya Nabil Sayed (17317006)Yassine Himeur (14158821)Faycal Bensaali (12427401)EngineeringElectronics, sensors and digital hardwareEnvironmental engineeringInformation and computing sciencesArtificial intelligenceData management and data scienceMachine learningOccupancy detectionEnvironmental dataFeature engineeringImage transformationDeep learningConvolutional neural network<p dir="ltr">Building occupancy information could aid energy preservation while simultaneously maintaining the end-user comfort level. Energy conservation becomes essential since energy resources are scarce and human dependency on appliances is only exponentially increasing. While intrusive sensors (i.e., cameras and microphones) can raise privacy concerns, this paper presents an innovative non-intrusive occupancy detection approach using environmental sensor data (e.g., temperature, humidity, carbon dioxide (CO<sub>2</sub>), and light sensors). The proposed scheme transforms multivariate time-series data into images for better encoding and extracting relevant features. The utilized image transformation method is based on data normalization and matrix conversion. Precisely, by representing time-series in 2D space, an encoding kernel can move in two directions while it can move only in one direction when applied to a 1D signal. Moreover, machine learning (ML) and deep learning (DL) techniques are utilized to classify occupancy patterns. Several simulations are used to evaluate the approach; mainly, we investigated pre-trained and custom convolutional neural network (CNN) models. The latter attained an accuracy of 99.00%. Additionally, pixel data are extracted from the generated images and subjected to traditional ML methods. Throughout the numerous comparison settings, it was observed that the latter strategy provided the optimal balance of 99.42% accuracy performance and minimal training time across the occupancy datasets.</p><h2>Other Information</h2><p dir="ltr">Published in: Engineering Applications of Artificial Intelligence<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.engappai.2022.105786" target="_blank">https://dx.doi.org/10.1016/j.engappai.2022.105786</a></p>2023-03-01T18:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.engappai.2022.105786https://figshare.com/articles/journal_contribution/From_time-series_to_2D_images_for_building_occupancy_prediction_using_deep_transfer_learning/24474652CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/244746522023-03-01T18:00:00Z |
| spellingShingle | From time-series to 2D images for building occupancy prediction using deep transfer learning Aya Nabil Sayed (17317006) Engineering Electronics, sensors and digital hardware Environmental engineering Information and computing sciences Artificial intelligence Data management and data science Machine learning Occupancy detection Environmental data Feature engineering Image transformation Deep learning Convolutional neural network |
| status_str | publishedVersion |
| title | From time-series to 2D images for building occupancy prediction using deep transfer learning |
| title_full | From time-series to 2D images for building occupancy prediction using deep transfer learning |
| title_fullStr | From time-series to 2D images for building occupancy prediction using deep transfer learning |
| title_full_unstemmed | From time-series to 2D images for building occupancy prediction using deep transfer learning |
| title_short | From time-series to 2D images for building occupancy prediction using deep transfer learning |
| title_sort | From time-series to 2D images for building occupancy prediction using deep transfer learning |
| topic | Engineering Electronics, sensors and digital hardware Environmental engineering Information and computing sciences Artificial intelligence Data management and data science Machine learning Occupancy detection Environmental data Feature engineering Image transformation Deep learning Convolutional neural network |